Another thread started by @Darryl Lee gets at a very real enterprise concern:
“Has anyone actually hit the indexed object limits when connecting large systems like SharePoint, OneDrive, or Google Drive?”
What teams are actually experiencing
Short Answer: Most enterprises do not hit the limit first; they hit indexing quality and scope challenges before they ever reach the cap.
Even in very large environments with tens of millions of files, teams typically run into:
- slow or partial indexing
- permission-based gaps
- inconsistent coverage across repositories
- difficulty controlling what should be indexed
So while the limits look restrictive on paper, they are not usually the first blocker in practice.
Why the numbers look more alarming than they are
In large environments, raw file counts can be massive, but not all of these become indexed objects.
Indexed objects are filtered by scope, permissions, and supported content types, so the actual indexed volume is much lower than total file counts.
What reduces the count:
- selected sites, folders, or libraries
- supported file types only
- user-level permissions
- connector configuration
So even very large environments rarely translate into full indexing of all content.
What happens when limits are approached
Indexing slows, becomes selective, or stops expanding rather than failing all at once.
If a team gets close to the allowance:
- new content may not be indexed
- existing indexed content continues to work
- there is no overage billing today... (but may happen someday)
The real enterprise challenge is not limits
The bigger issue is signal versus noise.
When organizations try to index everything:
- results become less relevant
- duplicate or low-value content surfaces
- permissions create fragmented visibility
This leads to a poor user experience long before limits become the primary concern.
What experienced teams are doing instead
Successful implementations tend to:
- start with high-value content (KBs, curated docs, key repositories)
- limit connector scope intentionally
- avoid indexing entire drives or intranets by default
- refine based on usage and gaps
This approach improves both performance and adoption.
Where limits still matter
Limits become relevant when:
- large-scale ingestion is attempted without filtering
- multiple large connectors are combined
- organizations expect full parity with their entire document ecosystem
In those cases, limits act as a forcing function to prioritize.
Champion takeaway
Enterprises are connecting large data sources to Rovo, but the first constraint they hit is not the indexed object limit. It is how to structure, filter, and govern the data so Rovo returns useful results.
If you are planning to connect SharePoint or Google Drive at scale, the right question is not “Can we index everything?” It is “What should we index to get the best results?”
